Multiple sclerosis (MS) is a common and severe disorder of the central nervous system characterized by chronic inflammation, myelin loss, gliosis, varying degrees of axonal and oligodendrocyte pathology, and progressive neurological dysfunction. MS pathogenesis includes a complex genetic component. In spite of intensive long-standing efforts, the knowledge of MS genetics remains incomplete. Our overall objective is to characterize the repertoire of genes that predispose to MS and modulate its presentation. Their identification is now possible as a result of rapid progress in defining the landscape of genetic organization and cataloging variation across the human genome. This proposal builds on the availability of second generation, high-quality genome-wide association results and comprehensive phenotypic data in a large MS cohort. We propose four main research goals: In Specific Aim 1 high-coverage genome-wide genetic data from a total of approximately 17,000 subjects affected with MS will be pooled and analyzed using a multi-stage and multi- analytical approach to map unambiguous association signals from sequence (SNPs) and copy number (CNVs) polymorphisms and identify novel disease candidate genes, leading to robust and testable hypotheses as to which are the specific common allelic variants conferring susceptibility. Using the meta-analysis-derived genotypes, together with open databases and novel algorithms, we will develop a composite global map of the effects of epistatic interactions and biochemical networks of genomic variation underlying MS pathogenesis. Specific Aim 2 takes advantage of the wealth of phenotypic data available for the different datasets to assess disease course variables and correlations to genotype. Important clinical metrics such as age and site of disease onset, disability at entry of study and progression, and changes in lesion distribution and burden will be incorporated into the analysis of genetic data. This aim directly addresses the question of clinical heterogeneity in MS and the correlation between different phenotypes and genotypes. In Specific Aim 3 we intend to generate high-coverage sequence information for the regulatory regions, exons and flanking regions of genes with unequivocal evidence of association for the discovery of rare variants associated with MS. To efficiently resequence DNA in a large dataset (3,000 patients / 3,000 controls), we will create 120 pools of 50 DNA samples each. Approximately 1,000 low-frequency variants will be genotyped in a validation cohort consisting of 10,000 MS subjects and 10,000 control subjects. As technologies advance and costs retreat, whole exome re-sequencing will be pursued in the second half of the funding cycle. Finally, in Specific Aim 4 we will integrated all the generated data and build an array, the MS Fine-Mapping Gene-chip, consisting of a comprehensive compendium of common and rare variants covering and flanking confirmed associations with susceptibility and disease expression. All relevant variants will be tested in an independent dataset (10,000 cases / 10,000 controls) for association individually and by combining multiple alleles within a single gene and across multiple genes to assess of causative cumulative effects. From this dataset, we expect to generate a minimal set of DNA variants that individually or in combination can aid in prediction of disease risk and/or progression. The availability of a large and well-characterized cohort as described here, coupled with the aid of high-powered laboratory technologies, provides an outstanding opportunity to identify and characterize MS-related genes. This information may translate into clinically useful genetic biomarkers and reveal novel targets for therapy.